In order to save a bandwidth for transmitting and storing voice and audio signals, a corresponding voice and audio coding technology has been widely applied. Currently, lossy coding and lossless coding are mainly divided. A reconstructed signal and an original signal in the lossy coding cannot be completely consistent with each other, but redundant information of a signal may be reduced to the maximum extent according to a feature of a sound source and a perception feature of human-beings, a small amount of coding information is transmitted, and higher voice and audio quality is reconstructed. For the lossless coding, it must be ensured that the reconstructed signal is completely consistent with the original signal, so that quality of the last decoding may not be deteriorated at all. Generally speaking, a compression rate of the lossy coding is higher, but the voice reconstruction quality is not ensured. Because the lossless coding may reconstruct a signal without distortion, voice quality may be ensured, but the compression rate is lower. A common lossless compression coder includes a short-term linear prediction coder (LPC, linear prediction coding), a long term predictor (LTP, Long Term Prediction), and an entropy coder. LPC prediction is used to remove short-term dependence of a voice signal, and LTP is used to remove long-term dependence of the voice signal, so as to improve the compression efficiency.
However, an existing conventional prediction method cannot be applicable to all types of input signals, and for some signals, by using these common prediction means, no compression gain can be obtained. For example, an input signal that has a larger dynamic range and changes rapidly in a time domain, and has a white noise-like spectrum in a frequency spectrum is difficult to be predicted and compressed, which severely affects compression efficiency of a voice and audio signal.